OpenRaiser/Intern-Atlas huggingface dataset:
Dataset and Analysis of Long-Term Skill Acquisition in Robot-Assisted Minimally Invasive Surgery
Venue: arXiv cs.RO 2025
Year: 2025

Objective: We aim to investigate long-term robotic surgical skill acquisition among surgical residents and the effects of training intervals and fatigue on performance. Methods: For six months, surgical residents participated in three training sessions once a month, surrounding a single 26-hour hospital shift. In each shift, they participated in training sessions scheduled before, during, and after the shift. In each training session, they performed three dry-lab training tasks: Ring Tower Transfer, Knot-Tying, and Suturing. We collected a comprehensive dataset, including videos synchronized with kinematic data, activity tracking, and scans of the suturing pads. Results: We collected a dataset of 972 trials performed by 18 residents of different surgical specializations. Participants demonstrated consistent performance improvement across all tasks. In addition, we found variations in between-shift learning and forgetting across metrics and tasks, and hints for possible effects of fatigue. Conclusion: The findings from our first analysis shed light on the long-term learning processes of robotic surgical skills with extended intervals and varying levels of fatigue. Significance: This study lays the groundwork for future research aimed at optimizing training protocols and enhancing AI applications in surgery, ultimately contributing to improved patient outcomes. The dataset will be made available upon acceptance of our journal submission.
PS-ReID: Advancing Person Re-Identification and Precise Segmentation with Multimodal Retrieval
Venue: arXiv cs.CV 2025
Year: 2025

Person re-identification (ReID) plays a critical role in applications such as security surveillance and criminal investigations. Most traditional image-based ReID methods face challenges including occlusions and lighting changes, while text provides complementary information to mitigate these issues. However, the integration of both image and text modalities remains underexplored. To address this gap, we propose {\bf PS-ReID}, a multimodal model that combines image and text inputs to enhance ReID performance. In contrast to existing ReID methods limited by cropped pedestrian images, our PS-ReID focuses on full-scene settings and introduces a multimodal ReID task that incorporates segmentation, enabling precise feature extraction of the queried individual, even under challenging conditions such as occlusion. To this end, our model adopts a dual-path asymmetric encoding scheme that explicitly separates query and target roles: the query branch captures identity-discriminative cues, while the target branch performs holistic scene reasoning. Additionally, a token-level ReID loss supervises identity-aware tokens, coupling retrieval and segmentation to yield masks that are both spatially precise and identity-consistent. To facilitate systematic evaluation, we construct M2ReID, currently the largest full-scene multimodal ReID dataset, with over 200K images and 4,894 identities, featuring multimodal queries and high-quality segmentation masks. Experimental results demonstrate that PS-ReID significantly outperforms unimodal query-based models in both ReID and segmentation tasks. The model excels in challenging real-world scenarios such as occlusion, low lighting, and background clutter, offering a robust and flexible solution for person retrieval and segmentation. All code, models, and datasets will be publicly available.
Leveraging Language Models for Analyzing Longitudinal Experiential Data in Education
Venue: arXiv cs.LG 2025
Year: 2025

We propose a novel approach to leveraging pre-trained language models (LMs) for early forecasting of academic trajectories in STEM students using high-dimensional longitudinal experiential data. This data, which captures students' study-related activities, behaviors, and psychological states, offers valuable insights for forecasting-based interventions. Key challenges in handling such data include high rates of missing values, limited dataset size due to costly data collection, and complex temporal variability across modalities. Our approach addresses these issues through a comprehensive data enrichment process, integrating strategies for managing missing values, augmenting data, and embedding task-specific instructions and contextual cues to enhance the models' capacity for learning temporal patterns. Through extensive experiments on a curated student learning dataset, we evaluate both encoder-decoder and decoder-only LMs. While our findings show that LMs effectively integrate data across modalities and exhibit resilience to missing data, they primarily rely on high-level statistical patterns rather than demonstrating a deeper understanding of temporal dynamics. Furthermore, their ability to interpret explicit temporal information remains limited. This work advances educational data science by highlighting both the potential and limitations of LMs in modeling student trajectories for early intervention based on longitudinal experiential data.
UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

The recent DeepSeek-R1 has showcased the emergence of reasoning capabilities in LLMs through reinforcement learning (RL) with rule-based rewards. Despite its success in language models, its application in multi-modal domains, particularly in graphic user interface (GUI) agent tasks, remains under-explored. To address this issue, we propose UI-R1, the first framework to explore how rule-based RL can enhance the reasoning capabilities of multimodal large language models (MLLMs) for GUI action prediction tasks. Specifically, UI-R1 introduces a novel rule-based action reward, enabling model optimization via policy-based algorithms such as Group Relative Policy Optimization (GRPO). For efficient training, we curate a small yet high-quality dataset of 136 challenging tasks, encompassing five common action types on mobile devices. Experimental results demonstrate that our proposed UI-R1-3B achieves significant improvements over the base model (i.e. Qwen2.5-VL-3B) on both in-domain (ID) and out-of-domain (OOD) tasks, with average accuracy gains of 22.1% on ScreenSpot, 6.0% on ScreenSpot-Pro, and 12.7% on ANDROIDCONTROL. Furthermore, UI-R1-3B delivers competitive performance compared to larger models (e.g., OS-Atlas-7B) trained via supervised fine-tuning (SFT) on 76K samples. We additionally develop an optimized version, UI-R1-E-3B, which significantly improves both grounding efficiency and accuracy. These results underscore the potential of rule-based reinforcement learning to advance GUI understanding and control, paving the way for future research in this domain. Code website: https://github.com/lll6gg/UI-R1.
The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, performance on existing anomaly detection benchmarks like MVTec AD and VisA has started to saturate in terms of segmentation AU-PRO, with state-of-the-art models often competing in the range of less than one percentage point. This lack of discriminatory power prevents a meaningful comparison of models and thus hinders progress of the field, especially when considering the inherent stochastic nature of machine learning results. We present MVTec AD 2, a collection of eight anomaly detection scenarios with more than 8000 high-resolution images. It comprises challenging and highly relevant industrial inspection use cases that have not been considered in previous datasets, including transparent and overlapping objects, dark-field and back light illumination, objects with high variance in the normal data, and extremely small defects. We provide comprehensive evaluations of state-of-the-art methods and show that their performance remains below 60% average AU-PRO. Additionally, our dataset provides test scenarios with lighting condition changes to assess the robustness of methods under real-world distribution shifts. We host a publicly accessible evaluation server that holds the pixel-precise ground truth of the test set (https://benchmark.mvtec.com/). All image data is available at https://www.mvtec.com/company/research/datasets/mvtec-ad-2.
RIS-Measurements for Codebook Design
Venue: arXiv cs.NI 2025
Year: 2025

Reconfigurable Intelligent Surfaces (RIS) have gained significant attention for some time. Thanks to the possibility of individual steering of each reflecting element of the boards, they are envisaged to impact the propagation environment significantly. In this work, we concentrate on the practical verification of this concept. We present the results of detailed measurements of the reflection characteristics of the RIS boards, which have been conducted intentionally in the real environment. Various potential impacting factors have been considered (impact of azimuth and elevation angle, polarization, number of RIS boards, and distance). Achieved measurement results constituted the basis for conceptual analysis on the practical possibility of creating a codebook (consisting of RIS patterns - codewords) for some applications.
ClusterSC: Advancing Synthetic Control with Donor Selection
Venue: arXiv cs.LG 2025
Year: 2025

In causal inference with observational studies, synthetic control (SC) has emerged as a prominent tool. SC has traditionally been applied to aggregate-level datasets, but more recent work has extended its use to individual-level data. As they contain a greater number of observed units, this shift introduces the curse of dimensionality to SC. To address this, we propose Cluster Synthetic Control (ClusterSC), based on the idea that groups of individuals may exist where behavior aligns internally but diverges between groups. ClusterSC incorporates a clustering step to select only the relevant donors for the target. We provide theoretical guarantees on the improvements induced by ClusterSC, supported by empirical demonstrations on synthetic and real-world datasets. The results indicate that ClusterSC consistently outperforms classical SC approaches.
KRAFT -- A Knowledge-Graph-Based Resource Allocation Framework
Venue: arXiv cs.SE 2025
Year: 2025

Resource allocation in business process management involves assigning resources to open tasks while considering factors such as individual roles, aptitudes, case-specific characteristics, and regulatory constraints. Current information systems for resource allocation often require extensive manual effort to specify and maintain allocation rules, making them rigid and challenging to adapt. In contrast, fully automated approaches provide limited explainability, making it difficult to understand and justify allocation decisions. Knowledge graphs, which represent real-world entities and their relationships, offer a promising solution by capturing complex dependencies and enabling dynamic, context-aware resource allocation. This paper introduces KRAFT, a novel approach that leverages knowledge graphs and reasoning techniques to support resource allocation decisions. We demonstrate that integrating knowledge graphs into resource allocation software allows for adaptable and transparent decision-making based on an evolving knowledge base.
Mapping the Digital Diplomatic Infrastructure: A Comparative Evaluation of Global Online Directories for Diplomatic Missions
Venue: arXiv cs.DL 2025
Year: 2025

This study provides a comparative evaluation of global diplomatic mission directories. DiplomaticMonitor.org, EmbassyPages.com, and WikiData.org are strategically selected among the top ten global services. After analyzing nearly all available online global diplomatic directory services, these three platforms are selected as they represent fundamentally different approaches to creating worldwide diplomatic mission databases. Using official diplomatic lists from over 150 countries as benchmarks, we assessed data coverage, accuracy, and update frequency across these platforms. DiplomaticMonitor consistently outperforms its counterparts in structure, completeness, and timeliness, accurately reflecting ambassadorial appointment cycles and maintaining high precision across contact and personnel records. EmbassyPages, despite strong search engine visibility and widespread usage, exhibits significant data currency issues, with markedly diminished ambassadorial accuracy attributable to delayed refresh cycles. WikiData offers valuable historical documentation and open-source accessibility but lacks the consistency and verification protocols necessary for reliable real-time diplomatic information. Our findings highlight the critical challenge posed by the absence of a standardized global diplomatic mission registry. In this fragmented landscape, methodologically rigorous third-party platforms can occasionally surpass government-published records in quality and utility. The research demonstrates that in contemporary digital diplomacy, data reliability correlates less with institutional provenance than with disciplined, transparent, and consistent data stewardship practices.
A tale of two goals: leveraging sequentiality in multi-goal scenarios
Venue: arXiv cs.LG 2025
Year: 2025

Several hierarchical reinforcement learning methods leverage planning to create a graph or sequences of intermediate goals, guiding a lower-level goal-conditioned (GC) policy to reach some final goals. The low-level policy is typically conditioned on the current goal, with the aim of reaching it as quickly as possible. However, this approach can fail when an intermediate goal can be reached in multiple ways, some of which may make it impossible to continue toward subsequent goals. To address this issue, we introduce two instances of Markov Decision Process (MDP) where the optimization objective favors policies that not only reach the current goal but also subsequent ones. In the first, the agent is conditioned on both the current and final goals, while in the second, it is conditioned on the next two goals in the sequence. We conduct a series of experiments on navigation and pole-balancing tasks in which sequences of intermediate goals are given. By evaluating policies trained with TD3+HER on both the standard GC-MDP and our proposed MDPs, we show that, in most cases, conditioning on the next two goals improves stability and sample efficiency over other approaches.
CMED: A Child Micro-Expression Dataset
Venue: arXiv cs.CV 2025
Year: 2025

Micro-expressions are short bursts of emotion that are difficult to hide. Their detection in children is an important cue to assist psychotherapists in conducting better therapy. However, existing research on the detection of micro-expressions has focused on adults, whose expressions differ in their characteristics from those of children. The lack of research is a direct consequence of the lack of a child-based micro-expressions dataset as it is much more challenging to capture children's facial expressions due to the lack of predictability and controllability. This study compiles a dataset of spontaneous child micro-expression videos, the first of its kind, to the best of the authors knowledge. The dataset is captured in the wild using video conferencing software. This dataset enables us to then explore key features and differences between adult and child micro-expressions. This study also establishes a baseline for the automated spotting and recognition of micro-expressions in children using three approaches comprising of hand-created and learning-based approaches.
SoK: Towards Reproducibility for Software Packages in Scripting Language Ecosystems
Venue: arXiv cs.SE 2025
Year: 2025

The disconnect between distributed software artifacts and their supposed source code enables attackers to leverage the build process for inserting malicious functionality. Past research in this field focuses on compiled language ecosystems, mostly analysing Linux distribution packages. However, the popular scripting language ecosystems potentially face unique issues given the systematic difference in distributed artifacts. This SoK provides an overview of existing research, aiming to highlight future directions, as well as chances to transfer existing knowledge from compiled language ecosystems. To that end, we work out key aspects in current research, systematize identified challenges for software reproducibility, and map them between the ecosystems. We find that the literature is sparse, focusing on few individual problems and ecosystems. This allows us to effectively identify next steps to improve reproducibility in this field.
Efficient Computation of the Directional Extremal Boundary of a Union of Equal-Radius Circles
Venue: arXiv cs.CG 2025
Year: 2025

This paper focuses on computing the directional extremal boundary of a union of equal-radius circles. We introduce an efficient algorithm that accurately determines this boundary by analyzing the intersections and dominant relationships among the circles. The algorithm has time complexity of O(n log n).
Not a nuisance but a useful heuristic: Outlier dimensions favor frequent tokens in language models
Venue: arXiv cs.CL 2025
Year: 2025

We study last-layer outlier dimensions, i.e. dimensions that display extreme activations for the majority of inputs. We show that outlier dimensions arise in many different modern language models, and trace their function back to the heuristic of constantly predicting frequent words. We further show how a model can block this heuristic when it is not contextually appropriate, by assigning a counterbalancing weight mass to the remaining dimensions, and we investigate which model parameters boost outlier dimensions and when they arise during training. We conclude that outlier dimensions are a specialized mechanism discovered by many distinct models to implement a useful token prediction heuristic.
Evaluating Text-to-Image and Text-to-Video Synthesis with a Conditional Fréchet Distance
Venue: arXiv cs.CV 2025
Year: 2025

Evaluating text-to-image and text-to-video models is challenging due to a fundamental disconnect: established metrics fail to jointly measure visual quality and semantic alignment with text, leading to a poor correlation with human judgments. To address this critical issue, we propose cFreD, a general metric based on a Conditional Fréchet Distance that unifies the assessment of visual fidelity and text-prompt consistency into a single score. Existing metrics such as Fréchet Inception Distance (FID) capture image quality but ignore text conditioning while alignment scores such as CLIPScore are insensitive to visual quality. Furthermore, learned preference models require constant retraining and are unlikely to generalize to novel architectures or out-of-distribution prompts. Through extensive experiments across multiple recently proposed text-to-image models and diverse prompt datasets, cFreD exhibits a higher correlation with human judgments compared to statistical metrics , including metrics trained with human preferences. Our findings validate cFreD as a robust, future-proof metric for the systematic evaluation of text conditioned models, standardizing benchmarking in this rapidly evolving field. We release our evaluation toolkit and benchmark.
Cylindrical Algebraic Decomposition in Macaulay2
Venue: arXiv cs.SC 2025
Year: 2025

CylindricalAlgebraicDecomposition.m2 is the first implementation of Cylindrical Algebraic Decomposition (CAD) in Macaulay2. CAD decomposes space into 'cells' where input polynomials are sign-invariant. This package computes an Open CAD (full-dimensional cells only) for sets of real polynomials with rational coefficients, enabling users to solve existential problems involving strict inequalities. With the construction of a full CAD (cells of all dimensions), this tool could be extended to solve any real quantifier elimination problem. The current implementation employs the Lazard projection and introduces a new heuristic for choosing the variable ordering.
Fwd2Bot: LVLM Visual Token Compression with Double Forward Bottleneck
Venue: arXiv cs.CV 2025
Year: 2025

In this work, we aim to compress the vision tokens of a Large Vision Language Model (LVLM) into a representation that is simultaneously suitable for (a) generative and (b) discriminative tasks, (c) is nearly lossless, and (d) is storage-efficient. We propose a novel compression approach, called Fwd2Bot, that uses the LVLM itself to compress the visual information in a task-agnostic manner. At the core of Fwd2bot there exists a "double-forward pass" training strategy, whereby, during the first forward pass, the LLM (of the LVLM) creates a bottleneck by condensing the visual information into a small number of summary tokens. Then, using the same LLM, the second forward pass processes the language instruction(s) alongside the summary tokens, used as a direct replacement for the image ones. The training signal is provided by two losses: an autoregressive one applied after the second pass that provides a direct optimization objective for compression, and a contrastive loss, applied after the first pass, that further boosts the representation strength, especially for discriminative tasks. The training is further enhanced by stage-specific adapters. We accompany the proposed method by an in-depth ablation study. Overall, Fwd2Bot results in highly-informative compressed representations suitable for both generative and discriminative tasks. For generative tasks, we offer a 2x higher compression rate without compromising the generative capabilities, setting a new state-of-the-art result. For discriminative tasks, we set a new state-of-the-art on image retrieval and compositionality.
Input-Triggered Hardware Trojan Attack on Spiking Neural Networks
Venue: arXiv cs.NE 2025
Year: 2025

Neuromorphic computing based on spiking neural networks (SNNs) is emerging as a promising alternative to traditional artificial neural networks (ANNs), offering unique advantages in terms of low power consumption. However, the security aspect of SNNs is under-explored compared to their ANN counterparts. As the increasing reliance on AI systems comes with unique security risks and challenges, understanding the vulnerabilities and threat landscape is essential as neuromorphic computing matures. In this effort, we propose a novel input-triggered Hardware Trojan (HT) attack for SNNs. The HT mechanism is condensed in the area of one neuron. The trigger mechanism is an input message crafted in the spiking domain such that a selected neuron produces a malicious spike train that is not met in normal settings. This spike train triggers a malicious modification in the neuron that forces it to saturate, firing permanently and failing to recover to its resting state even when the input activity stops. The excessive spikes pollute the network and produce misleading decisions. We propose a methodology to select an appropriate neuron and to generate the input pattern that triggers the HT payload. The attack is illustrated by simulation on three popular benchmarks in the neuromorphic community. We also propose a hardware implementation for an analog spiking neuron and a digital SNN accelerator, demonstrating that the HT has a negligible area and power footprint and, thereby, can easily evade detection.